multibert_1210seed85

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4043
• Precisions: 0.8689
• Recall: 0.8339
• F-measure: 0.8498
• Accuracy: 0.9067

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 7.5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 85
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Precisions	Recall	F-measure	Accuracy
0.5968	1.0	236	0.4307	0.8843	0.6749	0.7165	0.8690
0.3238	2.0	472	0.3849	0.8827	0.7215	0.7489	0.8916
0.2021	3.0	708	0.4067	0.8540	0.7640	0.7969	0.8983
0.1335	4.0	944	0.3857	0.8227	0.8002	0.8071	0.8983
0.0886	5.0	1180	0.4043	0.8689	0.8339	0.8498	0.9067
0.0654	6.0	1416	0.4734	0.8847	0.8016	0.8359	0.9089
0.0451	7.0	1652	0.5312	0.8215	0.7826	0.7996	0.8980
0.031	8.0	1888	0.5520	0.8730	0.7873	0.8222	0.9074
0.0248	9.0	2124	0.4954	0.8896	0.8145	0.8454	0.9149
0.0149	10.0	2360	0.5595	0.8717	0.8101	0.8354	0.9104
0.0086	11.0	2596	0.5703	0.8814	0.8051	0.8348	0.9112
0.0061	12.0	2832	0.5855	0.8655	0.8138	0.8356	0.9103
0.006	13.0	3068	0.6068	0.8578	0.8137	0.8329	0.9105
0.0039	14.0	3304	0.6147	0.8656	0.8129	0.8356	0.9112

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1